A comparison analysis of the decoupling carbon emissions from economic growth in three industries of Heilongjiang province in China.

PDF HTML阅读 XML下载 导出引用 引用提醒 基于LMDI分解的厦门市碳排放强度影响因素分析 DOI: 10.5846/stxb201304020585 作者: 作者单位: 中国科学院城市环境与健康重点实验室,中国科学院城市环境研究所,水利部珠江水利委员会,中国科学院城市环境与健康重点实验室,中国科学院城市环境研究所,中国科学院研究生院;中国科学院城市环境与健康重点实验室,中国科学院城市环境研究所,赤峰学院，资源与环境科学学院 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（71003090和71273252）；福建省自然科学基金资助项目（2012J01306） Factor decomposition of carbon intensity in Xiamen City based on LMDI method Author: Affiliation: Institute of Urban Environment, Chinese Academy of Sciences,,Institute of Urban Environment, Chinese Academy of Sciences,, Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:研究碳排放强度的变化趋势及其影响因素对于指导低碳城市建设具有重要意义。应用对数平均权重分解法（LMDI），基于厦门市2005-2010年各部门终端消费数据对碳排放强度指标进行因素分解，并将传统分析仅注重产业部门的能源碳排放，拓展到全面考虑产业部门和家庭消费的能源活动和非能源活动影响。研究结果表明：2005-2010年厦门市碳排放强度下降17.29%，其中产业部门能源强度对总碳排放强度变化影响最大（贡献63.07%），家庭消费能源强度是碳排放强度下降的主要抑制因素（-45.46%）。从影响效应角度看，经济效率对碳排放强度下降贡献最大，碳排系数减排贡献最小；从部门减排贡献角度看，第二产业贡献最大，家庭消费贡献最小。总体而言，厦门市未来碳减排重点部门在第二产业，优化产业结构和能源结构有较大减排潜力。 Abstract:It is of great significance for guiding the low-carbon city development to explore the trends and influencing factors of carbon intensity. Most traditional decomposition studies only focused on the energy carbon emissions from industrial sectors. This paper extended the application of the Logarithmic Mean weight Divisia Index (LMDI) method to a full consideration of the industrial and household sectors, as well as their energy and non-energy activities. Taking Xiamen City as a study case, the carbon emissions was calculated by IPCC's methods based on the end-use consumption data of the industrial and household sectors from 2005 to 2010. Then the aggregated carbon intensity was decomposed by LMDI method into ten driving factors, which covering energy and non-energy related emissions from industrial and household sectors. The ten driving factors were further categorized into four groups: carbon emission efficiency effect (including efficiency factors of energy related industrial carbon emissions, energy related household carbon emission, non-energy related industrial carbon intensity, and non-energy related household carbon intensity), energy intensity effect (including industrial energy intensity factor and that of household), industry structure effect (energy related industrial structure factor and non-energy one) and economic efficiency effect (energy related economic efficiency factor and non-energy one). Results showed that carbon intensity of Xiamen City decreased by 17.29% from 2005 to 2010. From perspective of driving factors, the energy intensity of industrial sector had the greatest effect on carbon intensity reduction (a contribution rate of 63.07%), and the energy intensity of household sector was the largest hinder of carbon intensity reduction (-45.46%). So energy intensity had significant impact on carbon intensity reduction for Xiamen City. Except for reducing the energy intensity of industrial sectors, it is also very important to control the growth of household's energy intensity at the same time. From the effect perspective, the economic efficiency effect became the dominant driver of carbon intensity reduction, followed by energy intensity effect and industry structure effect, and carbon emission efficiency effect contributed the less. The economic efficiency contributed 50.85% of total carbon intensity reduction, which greatly promoted household's carbon intensity reduction. Although industrial structure adjustment had relatively small effects at the study periods, the industry structure in which secondary industry has large proportion is anticipated to have large reduction potentials in the future. The carbon emission efficiency effect was chiefly determined by energy structure, and the current carbon-intensive energy structure also has large reduction potentials. From the sector perspective, the contribution of the secondary industry was the largest (contributing 67.04%), sequentially followed by the primary industry, the tertiary industry, and the household sector. The carbon intensity reduction by secondary and tertiary industries mainly lied in energy related carbon emissions; whereas the carbon intensity reduction by the primary industry and household sectors mainly relied on non-energy emissions. Thus the non-energy related carbon emissions were an non-negligible part while analyzing carbon intensity reduction. Even though energy efficiency of household sector was the biggest disincentive to reduce carbon intensity, household sector had the less contribution on carbon intensity reduction due to other factors' offset effect. Furthermore, the key sector for future carbon reduction lies on the secondary industry. However, the primary Industry and household sector has limited reduction potential. Overall, optimizing industry structure and energy structure have large reduction potential, and secondary industry has largest reduction potentials. 参考文献 相似文献 引证文献

Studies on carbon dioxide (CO2) emissions at provincial level can provide a scientific basis for the optimal use of energy and the formulation of CO2 reduction policies. We studied the variation of CO2 emissions of primary energy consumption and its influencing factors based on data in Xinjiang Uygur autonomous region from 1952 to 2008, which were calculated according to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Xinjiang’ CO2 emission process from 1952 to 2008 could be divided into five stages according to the growth rates of total amount of CO2 emissions and CO2 emission intensity. The impact factors were quantitatively analyzed using Logarithmic Mean Divisia Index (LMDI) method in each stage. Various factors, including government policies and technological progress related to the role of CO2 emissions, were comprehensively analyzed, and the internal relationships among various factors were clarified. The results show that the contribution rates of various impact factors are different in each stage. Overall, economic growth and energy consumption intensity were the main driving factors for CO2 emissions. Since the implementation of the birth control policy, the driving force of population growth on the increase in CO2 emissions has slowly weakened. The energy consumption intensity was further affected by the industrial structure and energy consumption intensity of primary, secondary and tertiary industries, with the energy consumption intensity of the secondary industries and the proportion of secondary industries being the most important factors affecting the energy consumption intensity. Governmental policies and technological progress were also important factors that affected CO2 emissions.

Resource-based regions are built upon the endowment of abundant natural resources; however, they often fall into development dilemmas due to the depletion of natural resources and ecological environmental regulations. How to achieve transformative development relying on the original industrial base is an important choice for the sustainable development of resource-based regions. This paper takes Heilongjiang Province, a resource-based province in China, as the research area and analyzes its process and strategies of transformative development from the perspective of industrial spatial patterns. The results show that: (1) There is spatial convergence in the development of secondary industry and industry in Heilongjiang Province from 2011 to 2020. The construction industry does not have spatial convergence, and the development of tertiary industry and its sub-industry does not have spatial convergence on the whole. (2) From 2011 to 2022, the development of secondary and tertiary industries in Heilongjiang Province formed a relatively stable spatial correlation network with good accessibility, but the hierarchy of network structure is not obvious, and the correlation strength and stability of the network need to be improved. (3) Harbin, Hegang, Qitaihe and other regions occupy a relatively central position in the spatial association network of the secondary industry; Harbin, Jiamusi, Suihua and other regions are in a leading position in the spatial association network of the tertiary industry which plays an important role as an intermediary bridge; other regions are in a relatively marginal position in the spatial association network of the industrial industry. (4) The increase in network density can effectively promote the development of the secondary and tertiary industries, and the network level and network efficiency will inhibit the development of the secondary and tertiary industries. The increase in network density will narrow the spatial difference of the secondary and tertiary industries, and the decrease in network level and network efficiency can effectively promote the spatial balance of the development of the secondary and tertiary industries. (5) The closer the spatial correlation between each region and other regions, the more benefits from the overall network, the more conducive to the development of local secondary and tertiary industries. The aforementioned results indicate that Heilongjiang Province is constructing a spatial pattern characterized by the complementarity of the primary, secondary and tertiary industries, which serves as a strategy for the transformative development of resource-based regions.

The Logarithmic Mean Divisia Index (LMDI) method is widely applied in research on carbon emissions, urban energy consumption, and the building sector, and is useful for theoretical research and evaluation. The approach is especially beneficial for combating climate change and encouraging energy transitions. During the method’s development, there are opportunities to develop advanced formulas to improve the accuracy of studies, as indicated by past research, that have yet to be fully explored through experimentation. This study reviews previous research on the LMDI method in the context of building carbon emissions, offering a comprehensive overview of its application. It summarizes the technical foundations, applications, and evaluations of the LMDI method and analyzes the major research trends and common calculation methods used in the past 25 years in the LMDI-related field. Moreover, it reviews the use of the LMDI in the building sector, urban energy, and carbon emissions and discusses other methods, such as the Generalized Divisia Index Method (GDIM), Decision Making Trial and Evaluation Laboratory (DEMATEL), and Interpretive Structural Modeling (ISM) techniques. This study explores and compares the advantages and disadvantages of these methods and their use in the building sector to the LMDI. Finally, this paper concludes by highlighting future possibilities of the LMDI, suggesting how the LMDI can be integrated with other models for more comprehensive analysis. However, in current research, there is still a lack of an extensive study of the driving factors in low-carbon city development. The previous related studies often focused on single factors or specific domains without an interdisciplinary understanding of the interactions between factors. Moreover, traditional decomposition methods, such as the LMDI, face challenges in handling large-scale data and highly depend on data quality. Together with the estimation of kernel density and spatial correlation analysis, the enhanced LMDI method overcomes these drawbacks by offering a more comprehensive review of the drivers of energy usage and carbon emissions. Integrating machine learning and big data technologies can enhance data-processing capabilities and analytical accuracy, offering scientific policy recommendations and practical tools for low-carbon city development. Through particular case studies, this paper indicates the effectiveness of these approaches and proposes measures that include optimizing building design, enhancing energy efficiency, and refining energy-management procedures. These efforts aim to promote smart cities and achieve sustainable development goals.

Left unmitigated, climate change poses a catastrophic risk to human health, requiring an urgent and concerted response from every country. As the home to one fifth of the world's population and the largest emitter of carbon dioxide globally, China's interventions in climate change are of pivotal importance, both to human health and to the planet. Similar to other countries, climate change mitigation and adaptation would bring immense health benefits for China's 1·4 billion people, and building these considerations into any COVID-19 recovery strategy and the detailed pathway to fulfil the 2060 carbon neutrality pledge will ensure it improves human wellbeing, both now and in the future. Decisions made over the coming months and years will establish the course of climate change policy for decades to come. To meet this challenge, Tsinghua University (Beijing, China), partnering with University College London (London, UK) and 17 Chinese and international institutions, has produced the Lancet Countdown China report, focusing at the national level and building on the work of the global Lancet Countdown. Drawing on international methods and frameworks, this report aims to understand and track the links between public health and climate change at the national level. This paper is one part of the Lancet Countdown's broader efforts to develop regional expertise and understanding. Uniquely, the data and results in this report are presented at the provincial level where possible, to facilitate the targeted response strategies for local decision makers. Taken as a whole, the findings of the 23 indicators convey two key messages. The first message is that the health effects from climate change in China are accelerating, posing an unacceptably high amount of health risk if global temperatures continue to rise. Every province is affected, each with its unique health threats, and targeted response strategies should be made accordingly. The effects of climate change, manifested in rising temperatures, more extreme weather events, and shifting vector ecology, are being felt in China. Heatwave-related mortality has risen by a factor of four from 1990 to 2019, reaching 26 800 deaths in 2019. The monetised cost of the high number of deaths is equivalent to the average annual income of 1·4 million people in China. Older people (>65 years old), who face a 10·4% higher risk of dying during a heatwave, endured an average of 13 more heatwave days in 2019 compared with the 1986–2005 baseline. For outdoor workers, their potential heat-related labour productivity loss reached 0·5% of total national work hours, costing 1% of China's gross domestic product (GDP), equivalent to its annual fiscal expenditure on science and technology. Driven in part by rising temperatures and a changing climate, the advent of more extreme wildfires and the spread of dengue fever will in turn lead to profound health effects. Different regions have unique health threats, requiring a targeted response—19 provinces have had an at least 10% rise over the past two decades in three or more of the six health effect indicators reported. Importantly, many highly populated and economically advanced provinces, such as Henan, Shandong, and Zhejiang, are faced with health risks that are larger and more rapidly accelerating than others. The second message is that impressive and concerted improvements have been made across several sectors in China; however, the gap in the country's response to the health effects of climate change is large. In some sectors, China has taken large steps to address climate change. Solar power generation is growing at an unprecedented rate of 26·5% per year, rising to 26·8 gigawatts (GW) of newly installed capacity in 2019. Investments in low-carbon energy are now nine times greater than those in fossil fuels (rising from a 1:1 ratio in 2008); and, providing 4·1 million jobs in 2018, renewable energy now employs more people in China than fossil fuel extraction industries. As a result of strong policy measures, severe air pollution has also decreased, with a 28% reduction in annual average particulate matter of 2·5 μm or less (PM2·5) concentration in cities from 2015 to 2019, resulting in 90 000 fewer PM2·5-related premature deaths annually. These air pollution control policies also act to mitigate climate change and have resulted in a decline in China's coal share in total primary energy supply from 66% in 2014 to 59% in 2018. Showing leadership at the subnational level, three provinces already have a provincial health and climate change plan in place, with four more provinces underway. However, although these changes have been rapid, more shifts of a greater size are necessary to enact a response that is of the scale required to fulfil China's carbon neutrality by 2060 pledge and to minimise the rising health burdens of climate change, both in China and around the world. Although renewable energy use is rising, coal stills holds a 59% share of the total primary energy supply in China. Fossil fuel subsidies were US$41·9 billion in 2018, without considering the contribution of fossil fuels to the estimated $10·7 billion economic losses because of premature mortality from PM2·5 air pollution. Although there have been substantial reductions in air pollution, 42% of China's population still live in areas that do not meet the interim air quality guidelines from WHO, and almost all cities have PM2·5 concentrations more than the recommended annual average of 10 μg/m3. The health effects of climate change are not adequately recognised or addressed, as climate change is not referenced in the Healthy China Action Plan (2019–30), and China is yet to introduce a standalone national adaptation plan for health. Taking a broader perspective, media coverage and individual engagement in health and climate change are low, with little spread of knowledge and engagement. China will need to scale up progress in all sectors to counteract the rising curve of the health risks from climate change. Five recommendations are proposed to key stakeholders in health and climate change in China: (1)Enhance interdepartmental cooperation. Climate change is a challenge that requires an integrated response from all sectors. Although China commits to integrate health into all policies, substantial interdepartmental cooperation among health, environment, energy, economic, financial, and education authorities is urgently needed.(2)Strengthen health emergency preparedness. Although the amount of health emergency preparedness in China would be greatly enhanced after COVID-19, knowledge and findings on current and future climate-related health threats still do not have enough attention and should be fully integrated into the emergency preparedness and response system, so that future health service, medical supplies, and infrastructure needs could be planned ahead.(3)Support research and raise awareness. Additional financial support should be allocated to health and climate change research in China, to enhance the knowledge of health system adaptation, mitigation measures, and their resulting health benefits. At the same time, media and academia should be fully motivated to raise awareness on this topic for the public and for politicians. Additionally, the Government of China should update the Healthy China Action Plan (2019–30) to address the health risks of climate change as soon as possible.(4)Increase climate change mitigation. China's new pledges towards carbon neutrality by 2060 is a major step forward. Speeding up the coal phase-out process is therefore necessary to be consistent with the carbon neutrality pledges and continue China's progress on air pollution reduction. Fossil fuel subsidies should also be phased out to reflect the true cost of ongoing fossil fuel use and to avoid undermining the effect of China's emissions trading scheme, scheduled to take effect in 2021.(5)Ensure the country's recovery from the COVID-19 pandemic protects health both now and in the future. Decisions made as part of China's efforts to recover from COVID-19 will shape the public's health for years to come. The longer-term prospects for lives, livelihoods, and a sustainable economy will be put in jeopardy if these interventions do not prioritise climate change.

Based on the energy consumption data of 30 provinces in China from 2000 to 2021, this paper estimates and predicts the total carbon emissions of 30 provinces in China from 2000 to 2035 using ARIMA model and BP neural network model. ArcGIS and standard elliptic difference are used to visually analyze the spatio-temporal evolution characteristics, and LMDI model is further used to decompose the driving factors affecting carbon emissions. The results show that: (1) China's total carbon emissions increased year by year from 2000 to 2035, but the growth rate of carbon emissions decreased gradually; The carbon emission structure is "secondary industry > residents' livelihood > tertiary industry > primary industry". the growth rate of carbon in secondary industry and residents' livelihood is relatively fast, while the change trend of primary industry and tertiary industry is relatively small. (2) the spatial distribution of carbon emissions in China's provinces presents a typical "eastern > central > western" and "northern > southern" distribution pattern, with the carbon emission center moving to the northwest; (3) The regions with higher development level of digital economy, industrial structure and new quality productivity have relatively less carbon emissions, with significant group difference effect; (4) Energy consumption intensity effect is the main factor to drive the continuous growth of carbon emissions, per capital GDP and energy consumption structure effect are the main factors to curb carbon emissions, and the impact of industrial structure and population size effect is relatively small. Based on the research conclusions, policy suggestions are put forward from the aspects of energy structure, industrial structure, new quality productivity and digital economy.

Assessment and prediction of net carbon emission from fishery in Liaoning Province based on eco-economic system simulation

China's carbon intensity factor decomposition and carbon emission decoupling analysis

To achieve the goals of carbon peaking and carbon neutrality in Shaanxi, the high energy consuming manufacturing industry (HMI), as an important contributor, is a key link and important channel for energy conservation. In this paper, the logarithmic mean Divisia index (LMDI) method is applied to determine the driving factors of carbon emissions from the aspects of economy, energy and society, and the contribution of these factors was analyzed. Meanwhile, the improved sparrow search algorithm is used to optimize Elman neural network (ENN) to construct a new hybrid prediction model. Finally, three different development scenarios are designed using scenario analysis method to explore the potential of HMI in Shaanxi Province to achieve carbon peak in the future. The results show that: (1) The biggest promoting factor is industrial structure, and the biggest inhibiting factor is energy intensity among the drivers of carbon emissions, which are analyzed effectively in HMI using the LMDI method. (2) Compared with other neural network models, the proposed hybrid prediction model has higher accuracy and better stability in predicting industrial carbon emissions, it is more suitable for simulating the carbon peaking process of HMI. (3) Only in the coordinated development scenario, the HMI in Shaanxi is likely to achieve the carbon peak in 2030, and the carbon emission curve of the other two scenarios has not reached the peak. Then, according to the results of scenario analysis, specific and evaluable suggestions on carbon emission reduction for HMI in Shaanxi are put forward, such as optimizing energy and industrial structure and making full use of innovative resources of Shaanxi characteristic units.

Possibilities of decoupling for China’s energy consumption from economic growth: A temporal-spatial analysis

Purpose: China is confronting with tremendous pressure in carbon emission reduction. While logistics industry seriously relies on fossil fuel, and emits greenhouse gas, especially carbon dioxide. The aim of this article is to estimate the carbon dioxide emission in China ’ s logistics sector, and analyze the causes for the change of carbon dioxide emission, and identify the critical factors which mainly drive the change in carbon dioxide emissions of China ’ s logistics industry . Design/methodology/approach: The logarithmic mean Divisia index (LMDI) method has often been used to analyze decomposition of energy consumption and carbon emission due to its theoretical foundation, adaptability, ease of use and result interpretation. So we use the LMDI method to analyze the changes in carbon dioxide emission in China ’ s logistics industry in this paper . Findings: By analyzing carbon dioxide emission of China ’ s logistics, the results show that the carbon dioxide emission of logistics in China has increased by 21.5 times, from 45.1 million tons to 1014.1 million tons in the research period. The highway transport is the main contributor to carbon dioxide emission in logistics industry. The energy intensity and carbon dioxide emission factors were contributing to the reduction of carbon dioxide emission in China ’ s logistics industry in overall study period. Originality/value: Although there are a lot of literature analyzed carbon dioxide emission in many industry sectors, for example manufacturing, iron and steel , pulp and paper, cement, glass industry, and so on. However, few scholars researched on carbon dioxide emission in logistics industry. This the first study is in the context of carbon dioxide emission of China ’ s logistics industry.

An in-depth study of the energy related carbon emissions has important practical significance for carbon emissions reduction and structural adjustment in Shandong Province and throughout China. Based on the perspective of industrial structure, the expanded KAYA equation to measure the energy related carbon emissions of the primary industries (Resources and Agriculture) and secondary industries (Manufacturing and Construction) and tertiary industries (Retail and Service) was utilized in Shandong Province from 2011 to 2017. The carbon emissions among industries in Shandong Province were empirically analyzed using the Logarithmic Mean Divisia Index decomposition approach. The results were follows: (1) Under the three industrial dimensions, the energy structure effect and the energy intensity effect have a restraining influence on the carbon emissions of the three industries. (2) The development level effect and the employment scale effect play a pulling role in carbon emissions. (3) From the perspective of the employment structure effect of the primary industry, there is a restraining effect on carbon emissions, while the employment structure effects of the secondary and tertiary industries play a pulling role in carbon emissions, and the employment structure effect of the tertiary industry has a greater pulling effect on carbon emissions than the secondary industry.

The 2021 China report of the Lancet Countdown on health and climate change: seizing the window of opportunity

Water issue is one of the challenges of urban sustainability in developing countries. To address the conflict between urban water use and economic development, it is required to better understand the decoupling states between them and the driving forces behind these decoupling states. The transformed Tapio decoupling model is applied in this paper to study the decoupling relationship between urban industrial water consumption and economic growth in Beijing and Shanghai, two megacities in China, in 2003–2016. The factors driving decoupling are divided into industrial structure effect, industrial water utilization intensity effect, economic development level effect, and population size effect through Logarithmic Mean Divisia Index (LMDI) method. The results show that: (1) the decoupling states of total water consumption and economic growth in Beijing and Shanghai are mainly strong decoupling and weak decoupling. In comparison, Shanghai’s decoupling effect is better than Beijing; (2) regarding decoupling elasticity, Beijing is higher than that of Shanghai in tertiary industry and lower in primary industry and secondary industry. As a result, Beijing’s decoupling level is worse than Shanghai in tertiary industry, while better in primary industry and secondary industry; (3) The common factors that drive the two megacities’ decoupling are industrial structure effect and industrial water utilization intensity effect. The effects of economic development level and population size mainly present weak decoupling in two megacities, but the decoupling state is optimized year by year. Finally, based on the results, some suggestions for achieving the sustainable development of urban water use are proposed.

Energy consumption is the major source of industrial carbon emissions. Energy consumption carbon emission factor method and LMDI (Logarithmic Mean Divisia Index) method was used to analyze the carbon emission evolution of industrial economy energy consumption in Jiangsu Province with collected data on industrial energy consumption in 1995-2012. Results showed that Jiangsu province economic industrial carbon emissions keep increasing in 1995-2012 years. The results of carbon emission increase analysis of energy consumption structure effects, industrial energy consumption intensity effects and output scale effects in 1999-2012 showed that energy consumption intensity effect has the maximum contribution to carbon emissions in industrial carbon emissions Jiangsu Province. Therefore, the main way to control carbon emissions of industrial energy consumption in Jiangsu Province is reasonably control the growth of energy consumption.